pca

Principle Component Analysis

• Reduce dimensionality, capture large variation with fewer features, especially when features are highly correlated.
• PCA uses an orthogonal transformation to convert a set of observations of possibly correlated variables into linearly uncorrelated variables.
• Individual explained variance and cumulative explained variance.
• $O(n^3)$ in terms of dimensionality